PRODUCTION SCHEDULING OPTIMIZATION MODEL BASED ON DYNAMIC PROGRAMMING AND GENETIC ALGORITHM

In view of the problem that enterprises can not improve their profits due to the difficulty in planning reasonable decision-making schemes in real life, this paper proposes a dynamic programming model of production decision-making based on genetic algorithm. Firstly, a joint target benefit model considering the detection cost of parts, the purchase price of parts, semi-finished products, the assembly cost of finished products, the loss of replacement, semi-finished products, the disassembly cost of finished products, the disassembly of semi-finished products into parts, the disassembly compensation of finished products into semi-finished products, and the market price is established. Then, the genetic algorithm is used to optimize the variables of whether to detect parts, whether to detect semi-finished products, and whether to disassemble finished products ( 0-1 ). Finally, taking an actual production plan as an example, the validity of the model is verified, and the decision-making plan when the profit is the largest is obtained. This paper proposes a genetic algorithm-based dynamic programming model for production decision-making, which comprehensively considers various cost and price factors to optimize decision variables, thereby improving production efficiency and profit margins, and is verified to be valid through an actual production plan.